The broad adoption of MFT was driven, in part, by advances in natural language processing (NLP), which enabled researchers to quantify moral values expressed in text, including news [1]}, [2]}, [3]}, political speech [4]}, and social media discussions [5]}, at scale. Early works relied on lexicons that defined words associated with moral virtues and vices to classify moral foundations from text [6]}. However, by neglecting semantic context in sentences, lexicon-based approaches fail to capture the nuances of moral expression [7]}. To address this challenge, more recent approaches use large language models to capture the moral context of text [8]}, [9]}. These approaches leverage a text corpus manually annotated for moral values to train language models to recognize examples of moral language. Several such ground truth data sets exist [5]}, [2]}, [12]}, [13]}, [3]}.
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